Bayesian model selection in complex linear systems, as illustrated in genetic association studies.
Bayesian model selection in complex linear systems, as illustrated in genetic association studies.
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作者：
Wen X
Motivated by examples from genetic association studies, this paper considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating context-dependent a priori information through different levels of prior specifications. We also derive analytic Bayes factors and their approximations to facilitate model selection and discuss their theoretical and computational properties. We demonstrate our Bayesian approach based on an implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real data application of mapping tissue-specific eQTLs. Our novel results on Bayes factors provide a general framework to perform efficient model comparisons in complex linear model systems.
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影响因子：
4.5
作者：
通讯作者：
--
DOI：
10.1198/jcgs.2010.09188
发表时间：
2010
期刊：
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
影响因子：
--
作者：
Rothman AJ;Levina E;Zhu J
通讯作者：
Zhu J
影响因子：
5.8
作者：
Wu, Tong Tong;Chen, Yi Fang;Lange, Kenneth
通讯作者：
Lange, Kenneth
影响因子：
2.1
作者：
Fridley, Brooke L.
通讯作者：
Fridley, Brooke L.
DOI：
10.1111/j.1467-9868.2005.00521.x
发表时间：
2005-01-01
影响因子：
5.8
作者：
Johnson, VE
通讯作者：
Johnson, VE